remotesensing-logo

Journal Browser

Journal Browser

Geospatial Monitoring on Local to Global Scale Impacts of Anthropogenic Landscape Changes

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 47071

Special Issue Editors

Forest Research Centre (CEF), School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Interests: remote sensing; entropy-based land use; urban sprawl; big data; land use and landcover; spatial interaction; urban and regional modeling; image entropy—google earth engine; R programming; GIS; urban GIS; forest fire monitoring; geomodeling; sustainable management; environment; agricultural sustainability; biodiversity monitoring; earth sciences; geography; ecology; forest ecology
Sustainable Landscapes and Restoration, World Resources Institute India, New Delhi 110016, India
Interests: earth observation science; GIS
Special Issues, Collections and Topics in MDPI journals
National Research Institute for Agriculture, Food and Environment (INRAE), UMR BAGAP, 65 rue de St-Brieuc CS 84215, CEDEX, 35042 Rennes, France
Interests: landscape ecology; land planning; remote sensing

Special Issue Information

Dear Colleagues,

Everything that has contributed to the development of modern human society is delivered by nature, and progressively, research determines the natural world’s incalculable significance to our health, wealth, food, and security. The worldwide economic movement eventually depends on different services provided by the environment and nature. A mounting human population and exploding human consumption have substantially degraded 75% of Earth’s land areas. These lands that have either become wastelands, are contaminated, or have been deforested and transformed to agricultural land are also the main driving force behind the unparalleled environmental transformation we are witnessing today, through the augmented demand for water, land, and energy. If this unsustainable growth continues, 95% of the Earth’s productive land areas might become degraded by 2050, which will force millions of people to migrate, as food production will break down in many places. Productive land degradation, loss of biodiversity, and climate alteration are diverse aspects of the same principal challenge: the increasingly hazardous influence of our choices on the health of our natural environment and ecosystem. There are three different types of landscape changes influenced by anthropogenic activities that have extensive impacts on the global climate and are interconnected with ecological and biophysical processes: Urbanization, geological changes, and forest degradation. The growing threat of climate change rapidly increases due to the decline of biodiversity by overexploitation of species, deforestation, agriculture, and land transformation. Climate change is distressing in all parts of the world: disrupting economies, affecting lives, altering the dynamics of entire species on the earth. Greenhouse gases are found to be one of the major reasons for the rising temperature and climate change. Research has evidenced that forests, including wetland forests, help to reduce the emission of greenhouse gases.

Researchers are developing sophisticated new tracking and systematic tools to match commodities and their supply chains to certain influences on the ecosystem. Improving transparency around these complex relationships may help to stop global climate change and biodiversity loss. To ensure the conservation, restoration, and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular, forests, wetlands, mountains, and drylands, it is important to find the existing techniques and understand the gaps in analyzing urbanization process, geological changes, and forest degradation associated with anthropogenic activities, which can help in landscape and climate-change-related planning.

This Special Issue aims to explore new challenges and gather relevant research work of novel applications that employ remote sensing techniques for quantification of local to global scale impacts of anthropogenic landscape changes.

Research contributions are welcome. In particular, novel contributions covering, but not limited to, the following subtopics are welcome:

  • Remotely sensed approach to monitor the urban heat island;
  • Spatial approach on forest fire investigation;
  • Impact of anthropogenic activities on environmental change;
  • Ecological effects of anthropogenic activities;
  • Influence of anthropogenic activity on forest cover;
  • Assessing urban sprawl from remotely sensed data;
  • Coastal wetland climate change and anthropogenic activities;
  • Soil, water, and air pollution.

Dr. Rajchandar Padmanaban
Dr. Parth Sarathi Roy
Dr. Jacques Baudry
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Urban heat island
  • Urban sprawl
  • Biophysical environment
  • Deforestation and forest fire
  • Ecosystem and biodiversity loss
  • Geological changes
  • Global warming
  • Pollution
  • Human overpopulation
  • Natural resource exploitation

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 2779 KiB  
Article
Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data
by Mukunda Dev Behera, Surbhi Barnwal, Somnath Paramanik, Pulakesh Das, Bimal Kumar Bhattyacharya, Buddolla Jagadish, Parth S. Roy, Sujit Madhab Ghosh and Soumit Kumar Behera
Remote Sens. 2021, 13(11), 2027; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112027 - 21 May 2021
Cited by 35 | Viewed by 5114
Abstract
Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition [...] Read more.
Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world. Full article
Show Figures

Graphical abstract

16 pages, 6464 KiB  
Article
Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India
by Pulakesh Das, Sujoy Mudi, Mukunda D. Behera, Saroj K. Barik, Deepak R. Mishra and Parth S. Roy
Remote Sens. 2021, 13(6), 1066; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061066 - 11 Mar 2021
Cited by 30 | Viewed by 3738
Abstract
Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for [...] Read more.
Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management. Full article
Show Figures

Graphical abstract

19 pages, 2407 KiB  
Article
Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values
by Wang Shuangao, Rajchandar Padmanaban, Aires A. Mbanze, João M. N. Silva, Mohamed Shamsudeen, Pedro Cabral and Felipe S. Campos
Remote Sens. 2021, 13(5), 851; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050851 - 25 Feb 2021
Cited by 17 | Viewed by 4241
Abstract
Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors [...] Read more.
Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors to assess the impact of landscape changes on ecosystem services (ES) and their economic values in the Long County, Shaanxi Province, China. We applied several landscape metrics to assess the local spatial configuration over 15 years (2004–2019) from fused imageries. Using Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI) and Indian Remote Sensing Satellite System Linear Imaging Self Scanning Sensor 3 (IRS LISS 3) imageries fused for 2004, 2009, 2014 and 2019, we reclassified land use/land cover (LULC) changes, through the rotation forest (RF) machine-learning algorithm. We proposed an equivalent monetary metric for estimating the ES values, which also could be used in the whole China. Results showed that agriculture farmland and unused land decreased their spatial distribution over time, with an observed increase on woodland, grassland, water bodies and built-up area. Our findings suggested that the patterns of landscape uniformity and connectivity improved, while the distribution of landscape types stabilized, while the landscape diversity had a slight improvement. The overall ES values increased (4.34%) under a benefit transfer approach, mainly concerning woodland and grassland. A sensitivity analysis showed the selected economic value (EV) was relevant and suitable for the study area associated with our ES for LULC changes. We suggested that changes in landscape patterns affected the ESV trends, while the increases on some LULC classes slightly improved the landscape diversity. Using an interdisciplinary approach, we recommend that local authorities and environmental practitioners should balance the economic benefits and ecological gains in different landscapes to achieve a sustainable development from local to regional scales. Full article
Show Figures

Graphical abstract

17 pages, 3017 KiB  
Article
Impacts of the Tropical Cyclone Idai in Mozambique: A Multi-Temporal Landsat Satellite Imagery Analysis
by Alberto Bento Charrua, Rajchandar Padmanaban, Pedro Cabral, Salomão Bandeira and Maria M. Romeiras
Remote Sens. 2021, 13(2), 201; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020201 - 08 Jan 2021
Cited by 32 | Viewed by 24926
Abstract
The Central Region of Mozambique (Sofala Province) bordering on the active cyclone area of the southwestern Indian Ocean has been particularly affected by climate hazards. The Cyclone Idai, which hit the region in March 2019 with strong winds causing extensive flooding and a [...] Read more.
The Central Region of Mozambique (Sofala Province) bordering on the active cyclone area of the southwestern Indian Ocean has been particularly affected by climate hazards. The Cyclone Idai, which hit the region in March 2019 with strong winds causing extensive flooding and a massive loss of life, was the strongest recorded tropical cyclone in the Southern Hemisphere. The aim of this study was to use pre- and post-cyclone Idai Landsat satellite images to analyze temporal changes in Land Use and Land Cover (LULC) across the Sofala Province. Specifically, we aimed—(i) to quantify and map the changes in LULC between 2012 and 2019; (ii) to investigate the correlation between the distance to Idai’s trajectory and the degree of vegetation damage, and (iii) to determine the damage caused by Idai on different LULC. We used Landsat 7 and 8 images (with 30 m resolution) taken during the month of April for the 8-year period. The April Average Normalized Difference Vegetation Index (NDVI) over the aforementioned period (2012–2018, pre-cyclone) was compared with the values of April 2019 (post-cyclone). The results showed a decreasing trend of the productivity (NDVI 0.5 to 0.8) and an abrupt decrease after the cyclone. The most devastated land use classes were dense vegetation (decreased by 59%), followed by wetland vegetation (−57%) and shrub land (−56%). The least damaged areas were barren land (−23%), barren vegetation (−27%), and grassland and dambos (−27%). The Northeastern, Central and Southern regions of Sofala were the most devastated areas. The Pearson Correlation Coefficient between the relative vegetation change activity after Idai (NDVI%) and the distance to Idai’s trajectory was 0.95 (R-square 0.91), suggesting a strong positive linear correlation. Our study also indicated that the LULC type (vegetation physiognomy) might have influenced the degree of LULC damage. This study provides new insights for the management and conservation of natural habitats threatened by climate hazards and human factors and might accelerate ongoing recovery processes in the Sofala Province. Full article
Show Figures

Figure 1

22 pages, 5614 KiB  
Article
Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing
by Md Moniruzzaman, Praveen K. Thakur, Pramod Kumar, Md. Ashraful Alam, Vaibhav Garg, Iman Rousta and Haraldur Olafsson
Remote Sens. 2021, 13(1), 83; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010083 - 29 Dec 2020
Cited by 45 | Viewed by 7297
Abstract
Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the [...] Read more.
Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km2 (1978) and 318.3 km2 (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km2 (24.44%) to 174.23 km2 (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces. Full article
Show Figures

Figure 1

Back to TopTop